3D sparse conv.
We leverage the Minkowski Net [1]} as the 3D sparse convolution network of choice. However, this type of sparse convolution is not designed for generative tasks of fine-grained details, which are the scope of this work. In a typical sparse convolution learning paradigm, the output itself is not a sparse voxel grid, but rather a point cloud or a continuous 3D prediction. In our setup, the output itself is a sparse voxel grid of radiance fields that have irregular structures and low-density components that cannot be seen when they are volume rendered yet they affect the SRF learning. We use residual connections in a U-Net form of Minkowski Net \(\mathbf {F}\)  with \(l\)  modules, where each module consists of 3 sparse convolutions layers with strides \(s\) .
